pytorch-unsupervised-segmentation
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kanezaki
a problem about about FCN
Hello, Thanks for the paper & open sourcing the code very much. I noticed that in the paper you mentioned "...Note that these components for feature extraction are able to be replaced by alternatives such as fully convolutional networks (FCN) .. ", so I imported the fcn-8s-atonce model structure code, and tried to run it with pretrained parameters and with the default pytorch parameters. But both of the results are bad:
For the former, fcn-8s-atonce model with pretrained parameters, the output segmented graphs are like these:
the original image:
iteration 1:
iteration2:
iteration3:
And the following iterations result are just like iteration3, by the way, I deleted the limitation of the number of the output classes. It seems that overfit happens.
And For the latter one, fcn-8s-atonce model with default pytorch parameters, the output segmented graphs are like these:
the original image:
iteration 1:
iteration2:
iteration3:
iteration 4...14:
I think something wrong happened, and it seems that the backward propagation do not update the parameters effectively. So I wanna ask that if you have tried with fcn before, and can you give me some advice about it? Thank you very much !!!
